Symphony turns project work into isolated, autonomous implementation runs, allowing teams to manage work instead of supervising coding agents.
In this demo video, Symphony monitors a Linear board for work and spawns agents to handle the tasks. The agents complete the tasks and provide proof of work: CI status, PR review feedback, complexity analysis, and walkthrough videos. When accepted, the agents land the PR safely. Engineers do not need to supervise Codex; they can manage the work at a higher level.
Warning
Symphony is a low-key engineering preview for testing in trusted environments.
Symphony works best in codebases that have adopted harness engineering. Symphony is the next step -- moving from managing coding agents to managing work that needs to get done.
Tell your favorite coding agent to build Symphony in a programming language of your choice:
Implement Symphony according to the following spec: https://github.com/openai/symphony/blob/main/SPEC.md
Check out elixir/README.md for instructions on how to set up your environment and run the Elixir-based Symphony implementation, including the GitHub Projects v2 operator setup. You can also ask your favorite coding agent to help with the setup:
Set up Symphony for my repository based on https://github.com/openai/symphony/blob/main/elixir/README.md
For common startup, workspace, review-loop, CI, Hex, and sandbox failures, see the Symphony troubleshooting guide.
GitHub Projects deployments can optionally auto-promote validated PRs from Human Review to
Merging when CI is green and Codex has no latest-commit P1 findings. See
elixir/README.md for the opt-in config and
rollout controls.
The Elixir implementation can optionally keep repository-local lessons from failed runs in
.symphony/lessons.md. When enabled in WORKFLOW.md, Symphony recalls recent entries into future
agent prompts, but it never auto-commits the file.
This project is licensed under the Apache License 2.0.